5 papers
Demand Transfer Estimation at Scale via Restricted Logit Modeling
Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav +4
Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to…
Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy
Ravi Teja Chunduri, Srikaran Reddy Boya, Deep Narayan Mishra +3
Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual gover…
SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data
Shashank Kapadia, Deep Narayan Mishra, Sujal Reddy Alugubelli +3
We present SURGE, a streaming GPU encoding system deployed in production to generate embeddings for over 800 million texts across 40,000 logical partitions. Production embedding pi…
LEAP: Layer-wise Exit-Aware Pretraining for Efficient Transformer Inference
Shashank Kapadia, Deep Naryan Mishra, Sujal Reddy Alugubelli +4
Layer-aligned distillation and convergence-based early exit represent two predominant computational efficiency paradigms for transformer inference; yet we establish that they exhib…
Monodense Deep Neural Model for Determining Item Price Elasticity
Lakshya Garg, Sai Yaswanth, Deep Narayan Mishra +3
Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue man…